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GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

Kai Ma, Xue Li, Wei Chen, Chi Zhang, Xiaorui Wang
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210
41st International Conference on Parallel Processing (ICPP 2012), 2012

@article{magreen2012gpu,

   title={GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures},

   author={Ma, K. and Li, X. and Chen, W. and Zhang, C. and Wang, X.},

   year={2012}

}

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In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, the energy consumption is a major concern due to the large scale of such kind of systems. There are a few existing efforts that try to lower the energy consumption of GPU-CPU architectures, but they address either GPU or CPU in an isolated manner and thus cannot achieve maximized energy savings. In this paper, we propose GreenGPU, a holistic energy management framework for GPUCPU heterogeneous architectures. Our solution features a twotier design. In the first tier, GreenGPU dynamically splits and distributes workloads to GPU and CPU based on the workload characteristics, such that both sides can finish approximately at the same time. As a result, the energy wasted on idling and waiting for the slower side to finish is minimized. In the second tier, GreenGPU dynamically throttles the frequencies of GPU cores and memory in a coordinated manner, based on their utilizations, for maximized energy savings with only marginal performance degradation. Likewise, the frequency and voltage of the CPU are scaled similarly. We implement GreenGPU using the CUDA framework on a real physical testbed with Nvidia GeForce GPUs and AMD Phenom II CPUs. Experiment results show that GreenGPU achieves 21.04% average energy savings and outperforms several well-designed baselines.
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